Recent approaches for meta-learning can be roughly put into three categories: (i) metric learning for acquiring similarities [1]}, [2]}, [3]}; (ii) learning optimizers for gaining update rules [4]}, [5]}; and (iii) recurrent networks for reserving the memory [6]}, [7]}. In this work, we adopt the meta-learning algorithm that belongs to the category of learning optimizers. Specifically, inspired by [8]} which was originally designed for image classification, we adopt ridge regression, which is a closed-form solution to the optimization problem. The reason for using it is because, compared with the widely-used SGD [9]} in CNNs, ridge regression can propagate gradient efficiently, which is matched with the goal of fast mapping.
Through extensive experiments, we demonstrate that the proposed method is in the first echelon
regarding to speed for fast video object segmentation, while obtaining more accurate results without any post-processing.
